Payment transaction fraud continues to be a concern for consumers, merchants, payment processors, and issuers. One technique for evaluating a risk of fraud in a transaction involves analyzing the transaction to determine a transaction score as the transaction is processed (i.e., in real-time). The transaction score may indicate, for example, the likelihood that the transaction is fraudulent. In some systems, transactions with a transaction score above a defined threshold may be declined.
Although real-time analytics systems may be used to effectively detect fraud, such systems may have shortcomings. For example, updating a system with a new scoring model may be labor-intensive and require downtime (e.g., to shut down the system, load a new scoring model, and restart the system). In addition, some systems may not support evaluating a test version of the model on real-time data, which may make it difficult to determine the effectiveness or performance of a model before it is put into production.
Embodiments of the present invention address these problems and other problems individually and collectively.